Optimized imaging methods for species-level identification of food-contaminating beetles.

Identifying the exact species of pantry beetle responsible for food contamination, is imperative in assessing the risks associated with contamination scenarios. Each beetle species is known to have unique patterns on their hardened forewings (known as elytra) through which they can be identified. Currently, this is done through manual microanalysis of the insect or their fragments in contaminated food samples. We envision that the use of automated pattern analysis would expedite and scale up the identification process. However, such automation would require images to be captured in a consistent manner, thereby enabling the creation of large repositories of high-quality images. Presently, there is no standard imaging technique for capturing images of beetle elytra, which consequently means, there is no standard method of beetle species identification through elytral pattern analysis. This deficiency inspired us to optimize and standardize imaging methods, especially for food-contaminating beetles. For this endeavor, we chose multiple species of beetles belonging to different families or genera that have near-identical elytral patterns, and thus are difficult to identify correctly at the species level. Our optimized imaging method provides enhanced images such that the elytral patterns between individual species could easily be distinguished from each other, through visual observation. We believe such standardization is critical in developing automated species identification of pantry beetles and/or other insects. This eventually may lead to improved taxonomical classification, allowing for better management of food contamination and ecological conservation.

Comparing SVM and ANN based Machine Learning Methods for Species Identification of Food Contaminating Beetles.

Insect pests, such as pantry beetles, are often associated with food contaminations and public health risks. Machine learning has the potential to provide a more accurate and efficient solution in detecting their presence in food products, which is currently done manually. In our previous research, we demonstrated such feasibility where Artificial Neural Network (ANN) based pattern recognition techniques could be implemented for species identification in the context of food safety. In this study, we present a Support Vector Machine (SVM) model which improved the average accuracy up to 85%. Contrary to this, the ANN method yielded ~80% accuracy after extensive parameter optimization. Both methods showed excellent genus level identification, but SVM showed slightly better accuracy  for most species. Highly accurate species level identification remains a challenge, especially in distinguishing between species from the same genus which may require improvements in both imaging and machine learning techniques. In summary, our work does illustrate a new SVM based technique and provides a good comparison with the ANN model in our context. We believe such insights will pave better way forward for the application of machine learning towards species identification and food safety.

Correction: Species Identification of Food Contaminating Beetles by Recognizing Patterns in Microscopic Images of Elytra Fragments.

[This corrects the article DOI: 10.1371/journal.pone.0157940.].

Species Identification of Food Contaminating Beetles by Recognizing Patterns in Microscopic Images of Elytra Fragments.

A crucial step of food contamination inspection is identifying the species of beetle fragments found in the sample, since the presence of some storage beetles is a good indicator of insanitation or potential food safety hazards. The current pratice, visual examination by human analysts, is time consuming and requires several years of experience. Here we developed a species identification algorithm which utilizes images of microscopic elytra fragments. The elytra, or hardened forewings, occupy a large portion of the body, and contain distinctive patterns. In addition, elytra fragments are more commonly recovered from processed food products than other body parts due to their hardness. As a preliminary effort, we chose 15 storage product beetle species frequently detected in food inspection. The elytra were then separated from the specimens and imaged under a microscope. Both global and local characteristics were quantified and used as feature inputs to artificial neural networks for species classification. With leave-one-out cross validation, we achieved overall accuracy of 80% through the proposed global and local features, which indicates that our proposed features could differentiate these species. Through examining the overall and per species accuracies, we further demonstrated that the local features are better suited than the global features for species identification. Future work will include robust testing with more beetle species and algorithm refinement for a higher accuracy.